Abstract
In this paper a modular deep neural network architecture are applied for recognize persons based on the iris biometric measurement of humans. The modular neural network consists of three modules, each module work with a deep neural network. This paper works with the human iris database improved with image preprocessing methods, these methods make a cut of the area of interest allowing remove the noise around the human iris. The input to the modular deep neural network is the preprocessed iris images and the output is the person identified. The “Gating Network” integrator is used for the integration of the modules for obtain the final results.
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Gaxiola, F., Melin, P., Valdez, F., Castro, J.R. (2018). Person Recognition with Modular Deep Neural Network Using the Iris Biometric Measure. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_6
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DOI: https://doi.org/10.1007/978-3-319-71008-2_6
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